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All Journal Mechatronics, Electrical Power, and Vehicular Technology Program Kreativitas Mahasiswa - Penelitian Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Ahmad Ataka Awwalur Rizqi, Ahmad Ataka
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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 1 
PERANCANGAN SISTEM KOORDINASI DAN KENDALI FORMASI UAV QUADROTOR UNTUK OPTIMALISASI MITIGASI BENCANA Pujantoro Tnunay, Ishak Hilton; Abdurrohman, Muhammad Qodar; Awwalur Rizqi, Ahmad Ataka; Faris, Muhammad; Puspita Sari, Dwi Retno
Program Kreativitas Mahasiswa - Penelitian PKM-P 2014
Publisher : Ditlitabmas, Ditjen DIKTI, Kemdikbud RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.248 KB)

Abstract

Indonesia is located between 2 continental plates in which causes the disaster often happens. One of the solution to encounter the mapping and searching after disaster is employing UAV robots. Quadrotor is one of these underlying robots that widely used due to its highly maneuverability, cheap, and less run-way. Instead of using single quadrotor which is less efficient and needs more time, we use multi-quadrotor system. These agents will coordinate to each other and form a formation, thereby it can be used to optimize the disaster monitoring process by multi-quadrotor. This work begins as follows: The study of formation and coordination of multi UAV system.  Afterwards, we design the path-planning and formation control algorithm based on potential fields for Quadrotor. Otherwise, we also employ visual navigation method combined with GPS in quadrotor to obtain its global position. In simulation, it can be showed that our approach works well. in the future we will apply it entirely in real quadrotor. Moreover, the results of this research have been published in some international conference, even receive a best paper award as well. Keywords:  Disaster Monitoring, Quadrotor, Visual Navigation, Path Planning, Formation Control
A Modified Gain Schedulling Controller by Considering the Sparseness Property of UAV Quadrotors Abdurrohman, M Qodar; Inovan, Reka; Ataka, Ahmad; Tnunay, Hilton; Wimbo, Ardhimas; Iswanto, Iswanto; Cahyadi, Adha; Yamamoto, Yoshio
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3347.433 KB) | DOI: 10.14203/j.mev.2015.v6.9-18

Abstract

This work presented the gain scheduling based LQR for Quadrotor systems. From the original nonlinear model, the system is always controllable and observable in various equilibrium points. Moreover, the linearized systems have a unique property that is known as sparse system. Hence, in order to implement the most efficient state feedback controller, post-filter and pre-filter were introduced to transform the state coordinate to decrease coupling between states. Finally, the gain scheduling systems using these facts was proposed. The system behavior was tested using the proposed controller. The numerical studies showed the effectiveness of the controller to achieve desired altitude, attitude, and its ability during the disturbance
A Modified Gain Schedulling Controller by Considering the Sparseness Property of UAV Quadrotors M Qodar Abdurrohman; Reka Inovan; Ahmad Ataka; Hilton Tnunay; Ardhimas Wimbo; Iswanto Iswanto; Adha Cahyadi; Yoshio Yamamoto
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.9-18

Abstract

This work presented the gain scheduling based LQR for Quadrotor systems. From the original nonlinear model, the system is always controllable and observable in various equilibrium points. Moreover, the linearized systems have a unique property that is known as sparse system. Hence, in order to implement the most efficient state feedback controller, post-filter and pre-filter were introduced to transform the state coordinate to decrease coupling between states. Finally, the gain scheduling systems using these facts was proposed. The system behavior was tested using the proposed controller. The numerical studies showed the effectiveness of the controller to achieve desired altitude, attitude, and its ability during the disturbance
Kendali Inverted Pendulum: Studi Perbandingan dari Kendali Konvensional ke Reinforcement Learning Ahmad Ataka; Andreas Sandiwan; Hilton Tnunay; Dzuhri Radityo Utomo; Adha Imam Cahyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i3.7065

Abstract

The rise of deep reinforcement learning in recent years has led to its usage in solving various challenging problems, such as chess and Go games. However, despite its recent success in solving highly complex problems, a question arises on whether this class of method is best employed to solve control problems in general, such as driverless cars, mobile robot control, or industrial manipulator control. This paper presents a comparative study between various classes of control algorithms and reinforcement learning in controlling an inverted pendulum system to evaluate the performance of reinforcement learning in a control problem. A test was performed to test the performance of root locus-based control, state compensator control, proportional-derivative (PD) control, and a reinforcement learning method, namely the proximal policy optimization (PPO), to control an inverted pendulum on a cart. The performances of the transient responses (such as overshoot, peak time, and settling time) and the steady-state responses (namely steady-state error and the total energy) were compared. It is found that when given a sufficient amount of training, the reinforcement learning algorithm was able to produce a comparable solution to its control algorithm counterparts despite not knowing anything about the system’s properties. Therefore, it is best used to control plants with little to no information regarding the model where testing a particular policy is easy and safe. It is also recommended for a system with a clear objective function.
Co-Authors Adha Cahyadi Adha Cahyadi, Adha Adha Imam Cahyadi Andreas Sandiwan Ardhimas Wimbo Ardhimas Wimbo, Ardhimas Dwi Retno Puspita Sari, Dwi Retno Dzuhri Radityo Utomo Hilton Tnunay Hilton Tnunay Hilton Tnunay, Hilton Ishak Hilton Pujantoro Tnunay, Ishak Hilton Iswanto Iswanto Iswanto Iswanto M Qodar Abdurrohman M Qodar Abdurrohman, M Qodar Muhammad Faris, Muhammad Muhammad Qodar Abdurrohman, Muhammad Qodar Reka Inovan Reka Inovan, Reka Yoshio Yamamoto Yoshio Yamamoto